CN106815661B - Decomposition coordination scheduling method of combined heat and power system - Google Patents

Decomposition coordination scheduling method of combined heat and power system Download PDF

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CN106815661B
CN106815661B CN201710097510.9A CN201710097510A CN106815661B CN 106815661 B CN106815661 B CN 106815661B CN 201710097510 A CN201710097510 A CN 201710097510A CN 106815661 B CN106815661 B CN 106815661B
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吴文传
张伯明
孙宏斌
蔺晨晖
郭庆来
王彬
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Tsinghua University
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Abstract

The invention relates to a decomposition coordination scheduling method of a combined heat and power system, and belongs to the technical field of power system operation. The method comprehensively considers a scheduling model of the power system and a scheduling model of the heat supply system, and establishes a combined heat and power optimization scheduling model. Aiming at the proposed combined heat and power optimization scheduling model, a decomposition coordination scheduling solving algorithm of a combined heat and power system is proposed based on a Benders decomposition algorithm. In the proposed thermoelectric combined optimization scheduling decomposition coordination algorithm, scheduling mechanisms of an electric power system and a heat supply system only need to optimize internal systems governed by the scheduling mechanisms, and a global optimal solution of thermoelectric combined optimization scheduling can be obtained through interactive iteration of boundary conditions between thermoelectricity. The decomposition coordination scheduling method of the combined heat and power system has good convergence rate and can obviously improve the operation flexibility of a heat supply system.

Description

Decomposition coordination scheduling method of combined heat and power system
Technical Field
The invention relates to a decomposition coordination scheduling method of a combined heat and power system, and belongs to the technical field of operation of power systems.
Background
The phenomenon of wind abandonment of an electric power system of a wind power collection area in northern China is very serious, and one main reason is that wind power has to be abandoned during strong wind at night to ensure the minimum heat supply of a heat supply system due to the limitation of the minimum output of a cogeneration unit. The current situation can be improved through combined heat and power optimization scheduling, and particularly, the heat supply load is adjusted in the time dimension by utilizing the heat storage benefit of an urban heat supply pipe network, so that the effect of peak clipping and valley filling is achieved. Therefore, during the high-power generation period of wind power at night, the cogeneration unit can reduce heat supply, further reduce the minimum power output of the cogeneration unit and provide a digestion space for the wind power.
The power system and the heat supply system are operated independently by a power company and a heat supply company respectively, and due to the scheduling independence, unified scheduling of complete models of the power system and the heat supply system is difficult to realize. Therefore, it is necessary to provide a decomposition coordination scheduling method for the cogeneration system. Specifically, the power system and the heat supply system can respectively perform scheduling optimization on the regions in the jurisdiction region, and the global optimal solution of the combined heat and power optimization scheduling is obtained through iteration of boundary conditions.
The Benders decomposition is a mathematical optimization algorithm that can decompose a complex optimization problem into several relatively simple optimization sub-problems, and the optimal solution of the original problem can be obtained by iteration of boundary conditions between different sub-problems.
Disclosure of Invention
The invention aims to provide a decomposition coordination scheduling method of a combined heat and power system. And secondly, considering the coupling constraints of the power system and the heat supply system, and establishing a combined heat and power optimization scheduling model. And aiming at the combined heat and power optimization scheduling model, based on a Benders decomposition algorithm, providing a decomposition coordination solving algorithm of the combined heat and power optimization scheduling model.
The invention provides a decomposition coordination scheduling method of a combined heat and power system, which comprises the following steps:
(1) establishing a scheduling model of the combined heat and power system, wherein the scheduling model consists of a target function and constraint conditions, and specifically comprises the following steps:
(1-1) objective function of scheduling model of combined heat and power system:
the objective function of the scheduling model of the combined heat and power system is the minimization of the operation cost of the power system and the heat supply system, and the expression is as follows:
Figure BDA0001230739190000021
in the above formula, T is a set of scheduling periods, ICHP、ITU、IWDAnd IHBRespectively are the number sets of a cogeneration unit, a conventional unit, a wind turbine unit and a heat boiler in a combined heat and power system,
Figure BDA0001230739190000022
and
Figure BDA0001230739190000023
the production cost functions of a cogeneration unit, a conventional unit, a wind turbine generator and a hot boiler in the combined heat and power system are respectively shown, i is the serial number of the cogeneration unit, the conventional unit, the wind turbine generator and the unit or the boiler in the hot boiler, and t is a scheduling time interval;
the production cost function of the cogeneration unit is as follows:
Figure BDA0001230739190000024
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000025
and
Figure BDA0001230739190000026
respectively are production cost coefficients which are intrinsic parameters of the cogeneration unit,
Figure BDA0001230739190000027
and
Figure BDA0001230739190000028
respectively the active power and the heat production quantity of the ith cogeneration unit in the t scheduling period;
the production cost function for a conventional unit is:
Figure BDA0001230739190000029
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000210
and
Figure BDA00012307391900000211
is the power generation cost coefficient of the conventional unit, which is the intrinsic parameter of the conventional unit,
Figure BDA00012307391900000212
the active power of the ith conventional unit in the t scheduling period is obtained;
the production cost function of the wind turbine generator is as follows:
Figure BDA00012307391900000213
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000214
the value of the wind abandon penalty factor is determined according to the consumption demand of the wind power, the dispatching center of the power system regulates according to the feedback of the dispatching result,
Figure BDA00012307391900000215
for the available active power of the ith wind power generation unit in the t scheduling period,
Figure BDA00012307391900000216
the actual active power of the wind turbine generator in the t-th scheduling period is obtained;
the production cost function of the thermal boiler is:
Figure BDA00012307391900000217
in the above formula，
Figure BDA00012307391900000218
Is the heat production cost coefficient of the heat boiler, is the inherent parameter of the heat boiler,
Figure BDA00012307391900000219
the heat production quantity of the ith heat boiler in the t scheduling period is obtained;
(1-2) constraints of a scheduling model of the cogeneration system, including:
(1-2-1) operating constraints of the power system in the cogeneration system:
the operation constraint conditions of the cogeneration unit are as follows:
Figure BDA0001230739190000031
Figure BDA0001230739190000032
in the above formula, NEiThe number set of the operation poles of the ith cogeneration unit is provided, wherein the operation poles refer to the points formed by the thermal output and the electric output limit of the cogeneration unit,
Figure BDA0001230739190000033
respectively the active power and the heat production quantity of the gamma operation pole of the ith cogeneration unit,
Figure BDA0001230739190000034
the convex combination coefficient of a gamma operation pole corresponding to the operation point of the ith cogeneration unit in the t scheduling period;
the climbing constraint conditions of the cogeneration unit are as follows:
Figure BDA0001230739190000035
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000036
and
Figure BDA0001230739190000037
respectively the upward climbing capacity and the downward climbing capacity of the ith cogeneration unit, wherein delta T is a scheduling time interval;
the conventional unit operation constraint conditions are as follows:
Figure BDA0001230739190000038
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000039
and
Figure BDA00012307391900000310
respectively setting the upper limit of active power and the lower limit of active power of the ith conventional unit;
the conventional unit climbing constraint conditions are as follows:
Figure BDA00012307391900000311
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000312
and
Figure BDA00012307391900000313
the climbing capacity and the climbing capacity of the ith conventional unit are respectively the climbing capacity upwards and the climbing capacity downwards;
the rotation standby constraint conditions of the conventional unit are as follows:
Figure BDA00012307391900000314
Figure BDA00012307391900000315
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000316
and
Figure BDA00012307391900000317
respectively carrying out upward rotation standby and downward rotation standby on the ith conventional unit in the t scheduling period;
the operation constraint conditions of the wind turbine generator are as follows:
Figure BDA00012307391900000318
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000319
for the available active power of the ith wind power generation unit in the t scheduling period,
Figure BDA00012307391900000320
the actual active power of the wind turbine generator in the t-th scheduling period is obtained;
the power balance constraint of the power system in the combined heat and power system is
Figure BDA00012307391900000321
In the above formula, ILDNumbering sets for loads of the power system, Dm,tThe active load size of the mth load in the tth scheduling period;
the constraint conditions of the line transmission capacity of the power system in the combined heat and power system are as follows:
Figure BDA0001230739190000041
in the above formula, IEPSRepresenting a set of node numbers in the power system,
Figure BDA0001230739190000042
and
Figure BDA0001230739190000043
index number sets respectively representing cogeneration units, conventional units, wind turbines and loads connected to the ith node of the power system, ILNRepresenting a set of power system line numbers, LjRepresenting the active transmission capacity of the jth line of the power system;
the rotating standby constraint conditions of the power system are as follows:
Figure BDA0001230739190000044
in the above formula, SRUtAnd SRDtRespectively representing an upward rotation standby and a downward rotation standby of the power system in the t scheduling period;
(1-2-2) operating constraints of a heating system in a combined heat and power system, including:
(1-2-2-1) heating constraint conditions of heat sources including a cogeneration unit and a heat boiler, including:
the constraint conditions of the temperature difference between the heat supply and the water supply and return of the nodes are as follows:
Figure BDA0001230739190000045
in the above formula, set
Figure BDA0001230739190000046
Representing a combined heat and power generation unit and a heat boiler number set connected with a heating system node k, C is the specific heat capacity of water,
Figure BDA0001230739190000047
is the water flow at node k,
Figure BDA0001230739190000048
and
Figure BDA0001230739190000049
respectively representing the supply and return water temperatures of node k, set
Figure BDA00012307391900000410
Representing a set of nodes connecting heat sources in a heating system;
constraint conditions of heat supply amount of the heat boiler:
Figure BDA00012307391900000411
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000412
representing the upper limit of the heat production amount of the ith heat boiler;
constraint conditions of supply water temperature of heat source nodes:
Figure BDA00012307391900000413
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000414
and
Figure BDA00012307391900000415
providing an upper limit and a lower limit of water supply temperature for a node k in a heating system;
(1-2-2-2) heat exchange station operating constraints comprising:
the relationship between the heat exchange quantity and the temperature difference between the water supply and the water return of the node is as follows:
Figure BDA00012307391900000416
in the above formula, set
Figure BDA0001230739190000051
Representing the set of heat exchange stations in the heating system connected to node k,
Figure BDA0001230739190000052
representing heat exchange quantity of the nth heat exchange station in the t scheduling period
Figure BDA0001230739190000053
Representing a node set connected with the heat exchange station in the heating system;
the return water temperature of the heat exchange station node needs to be ensured within a safety range:
Figure BDA0001230739190000054
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000055
and
Figure BDA0001230739190000056
respectively setting the upper limit and the lower limit of the return water temperature of a node k in the heating system;
(1-2-2-3) heating network operation constraints comprising:
Figure BDA0001230739190000057
Figure BDA0001230739190000058
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000059
and
Figure BDA00012307391900000510
respectively shows the water supply flow and the water return flow from the node k2 to the node k1 in the heating system,
Figure BDA00012307391900000511
and
Figure BDA00012307391900000512
respectively representing the supply pipe node and the return pipe node of node k1 in the heating system,
Figure BDA00012307391900000513
for the ambient temperature of the t-th scheduling period,
Figure BDA00012307391900000514
and
Figure BDA00012307391900000515
respectively shows the heat transfer coefficient of the water supply and the heat transfer coefficient of the water return flowing from the node k2 to the node k1 in the heating system,
Figure BDA00012307391900000516
and
Figure BDA00012307391900000517
the value of (A) is calculated by the following formula:
Figure BDA00012307391900000518
Figure BDA00012307391900000519
wherein the content of the first and second substances,
Figure BDA00012307391900000520
and
Figure BDA00012307391900000521
respectively, representing the unit heat transfer coefficients of the water supply pipe and the water return pipe flowing from the node k2 to the node k1 in the heating system, which can be obtained from the name plate of the water pipe,
Figure BDA00012307391900000522
and
Figure BDA00012307391900000523
respectively representing the lengths of the water supply pipe and the water return pipe flowing from the node k2 to the node k1 in the heating system.
Figure BDA00012307391900000524
And
Figure BDA00012307391900000525
the intermediate temperature variables of the heating system respectively represent the water supply temperature and the water return temperature flowing from the node k2 to the node k1 under the condition of not considering the temperature loss, and the expression is as follows:
Figure BDA00012307391900000526
Figure BDA00012307391900000528
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000530
respectively represents the number of the dispatching time periods required by the flow from the node k2 to the node k1 in the water supply pipe and the water return pipe in the heating system, and symbols
Figure BDA0001230739190000061
Represents rounding down;
(2) converting the combined heat and power optimization scheduling model established in the step (1) into a matrix form by using xERepresenting power system variables including
Figure BDA0001230739190000062
And
Figure BDA0001230739190000063
by xHRepresenting heating system variables including
Figure BDA0001230739190000064
And
Figure BDA0001230739190000065
the combined heat and power optimization scheduling model can be converted into a matrix form as follows:
Figure BDA0001230739190000066
s.t.AExE≤bE
AHxH≤bH
DxE+ExH≤f
in the above formula, CEAnd CHRespectively representing the objective functions of the power system and the heating system, wherein CERepresents
Figure BDA0001230739190000067
CHRepresents
Figure BDA0001230739190000068
Constraint AExE≤bERepresents the constraints of the power system, i.e., all the constraints in step (1-2-1), AE、bEEach row of (A) corresponds to each constraint condition of the power system, each column corresponds to each variable in the power system, wherein AEEach element of (b) is a coefficient of a variable corresponding to the column of the element in the constraint corresponding to the row of the element, bEThe element of each row of (1) is an inequality constant term in the constraint condition corresponding to the element; constraint AHxH≤bHRepresenting the constraint conditions of the heat supply system, namely all the constraint conditions except the relation between the heat supply quantity and the temperature difference between the supply water and the return water of the node in the step (1-2-2), AH、bHEach row of (A) corresponds to each constraint condition of the heating system, each column corresponds to each variable in the heating system, wherein AHEach element of (b) being a coefficient of a variable represented by its column in the constraint corresponding to the row of the element, bHThe element of each row of (1) is an inequality constant term in the constraint condition corresponding to the element, and the constraint DxE+ExHF is less than or equal to the coupling constraint condition of the power system and the heat supply system, namely the relationship constraint condition of the heat supply amount and the temperature difference of the water supply and return of the nodes in the step (1-2-2), each line of D, E, f is in one-to-one correspondence with each coupling constraint condition of the power system and the heat supply system, and each column of D is in one-to-one correspondence with the power systemEach variable in the system corresponds to one, each column of E corresponds to one of each variable in the heating system, wherein each element of D, E is a coefficient of the variable represented by the column of the element in the constraint condition corresponding to the row of the element, and the element of each row of f is an inequality constant term in the constraint condition corresponding to the element;
(3) solving the matrix-form combined heat and power optimization scheduling model obtained in the step (2) by adopting a decomposition coordination solving algorithm, wherein the steps are as follows:
(3-1) initialization: initializing the iteration number m to 0, initializing the optimal cutting number p to 0, initializing the feasible cutting number q to 0, solving the following optimal scheduling problem of the power system and obtaining the optimal solution
Figure BDA0001230739190000071
Figure BDA0001230739190000072
s.t.AExE≤bE
(3-2) solution according to electric Power System
Figure BDA0001230739190000073
The following heating system problems are solved:
Figure BDA0001230739190000074
s.t.AHxH≤bH
DxE+ExH≤f
Figure BDA0001230739190000075
(3-2-1) if the heating system problem in the step (3-2) is feasible, increasing the number p of optimal cuts by 1 to generate the optimal cuts as shown in the following formula:
Figure BDA0001230739190000076
wherein A isOC＝λT,
Figure BDA0001230739190000077
λ is in step (3-2)
Figure BDA0001230739190000078
The lagrange multiplier of the terms is,
Figure BDA0001230739190000079
the optimal objective function value of the heating system problem in the step (3-2);
(3-2-2) if the heating system problem in step (3-2) is not feasible, increasing the number q of feasible cuts by 1 to generate feasible cuts as shown in the following formula:
Figure BDA00012307391900000710
wherein the parameters
Figure BDA00012307391900000711
And
Figure BDA00012307391900000712
the generation steps are as follows:
(3-2-2-1) the feasibility problem of the heating problem is expressed in the form:
Figure BDA00012307391900000713
s.t.AHxH≤bH
Figure BDA00012307391900000714
(3-2-2-2) relaxation of the feasibility problem in the above step (3-2-2-1) is carried out by introducing a relaxation term, resulting in the following problems:
Figure BDA00012307391900000715
s.t.AHxH≤bH
Figure BDA00012307391900000716
≤0
(3-2-2-3) will bind to A in the relaxor problem in step (3-2-2-2) aboveHxH≤bHAnd constraint
Figure BDA0001230739190000081
The corresponding Lagrange multipliers are respectively marked as
Figure BDA0001230739190000082
And
Figure BDA0001230739190000083
then
Figure BDA0001230739190000084
And
Figure BDA0001230739190000085
respectively as follows:
Figure BDA0001230739190000086
(3-3) solving the power system optimization scheduling problem:
Figure BDA0001230739190000087
s.t.AExE≤bE
CH←E≥0
Figure BDA0001230739190000088
Figure BDA0001230739190000089
increasing the iteration number m by 1 and recording the optimal solution as
Figure BDA00012307391900000810
(3-4) judging the convergence of the iteration if
Figure BDA00012307391900000811
Wherein, if delta is a convergence threshold value and the value is 0.001, terminating iteration and executing the step (3-5); if it is
Figure BDA00012307391900000812
Returning to the step (3-2) to continue the calculation;
and (3-5) taking the obtained optimal solution as a scheduling parameter of the thermoelectric combined optimization scheduling.
The decomposition coordination scheduling method of the combined heat and power system has the advantages that:
the method comprehensively considers a scheduling model of the power system and a scheduling model of the heat supply system, and establishes a combined heat and power optimization scheduling model. Aiming at the proposed combined heat and power optimization scheduling model, a decomposition coordination scheduling solving algorithm of a combined heat and power system is proposed based on a Benders decomposition algorithm. In the proposed thermoelectric combined optimization scheduling decomposition coordination algorithm, scheduling mechanisms of an electric power system and a heat supply system only need to optimize internal systems governed by the scheduling mechanisms, and a global optimal solution of thermoelectric combined optimization scheduling can be obtained through interactive iteration of boundary conditions between thermoelectricity. The decomposition coordination scheduling method of the combined heat and power system has good convergence rate and can obviously improve the operation flexibility of a heat supply system.
Drawings
FIG. 1 is a schematic diagram of a typical cogeneration system.
FIG. 2 is a flow chart of a decomposition coordination scheduling iteration involved in the method of the present invention.
Detailed Description
The invention provides a decomposition coordination scheduling method of a combined heat and power system, wherein the structure of the related combined heat and power system is shown in figure 1, and the method comprises the following steps:
(1) establishing a scheduling model of the combined heat and power system, wherein the scheduling model consists of a target function and constraint conditions, and specifically comprises the following steps:
(1-1) objective function of scheduling model of combined heat and power system:
the objective function of the scheduling model of the combined heat and power system is the minimization of the operation cost of the power system and the heat supply system, and the expression is as follows:
Figure BDA0001230739190000091
in the above formula, T is a set of scheduling periods, ICHP、ITU、IWDAnd IHBRespectively are the number sets of a cogeneration unit, a conventional unit, a wind turbine unit and a heat boiler in a combined heat and power system,
Figure BDA0001230739190000092
and
Figure BDA0001230739190000093
the production cost functions of a cogeneration unit, a conventional unit, a wind turbine generator and a hot boiler in the combined heat and power system are respectively shown, i is the serial number of the cogeneration unit, the conventional unit, the wind turbine generator and the unit or the boiler in the hot boiler, and t is a scheduling time interval;
the production cost function of the cogeneration unit is as follows:
Figure BDA0001230739190000094
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000095
and
Figure BDA0001230739190000096
respectively is a production cost coefficient and a production cost systemThe numbers are intrinsic parameters of the cogeneration unit (taken from the unit description),
Figure BDA0001230739190000097
and
Figure BDA0001230739190000098
respectively the active power and the heat production quantity of the ith cogeneration unit in the t scheduling period;
the production cost function for a conventional unit is:
Figure BDA0001230739190000099
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000910
and
Figure BDA00012307391900000911
is the power generation cost coefficient of the conventional unit, which is the inherent parameter of the conventional unit (obtained from the unit specification),
Figure BDA00012307391900000912
the active power of the ith conventional unit in the t scheduling period is obtained;
the production cost function of the wind turbine generator is as follows:
Figure BDA00012307391900000913
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000914
the value of the wind abandon penalty factor is determined according to the consumption demand of the wind power, the dispatching center of the power system regulates according to the feedback of the dispatching result,
Figure BDA00012307391900000915
is available active power of the ith wind generating set in the t scheduling periodThe power of the electric motor is controlled by the power controller,
Figure BDA00012307391900000916
the actual active power of the wind turbine generator in the t-th scheduling period is obtained;
the production cost function of the thermal boiler is:
Figure BDA00012307391900000917
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900000918
the heat production cost coefficient of the heat boiler, the inherent parameters of the heat boiler (can be obtained from the specification of the heat boiler),
Figure BDA00012307391900000919
the heat production quantity of the ith heat boiler in the t scheduling period is obtained;
(1-2) constraints of a scheduling model of the cogeneration system, including:
(1-2-1) operating constraints of the power system in the cogeneration system:
the operation constraint conditions of the cogeneration unit are as follows:
Figure BDA0001230739190000101
Figure BDA0001230739190000102
in the above formula, NEiThe number set of the operation poles of the ith cogeneration unit is provided, wherein the operation poles refer to the points formed by the thermal output and the electric output limit of the cogeneration unit,
Figure BDA0001230739190000103
respectively the active power and the heat production quantity of the gamma operation pole of the ith cogeneration unit,
Figure BDA0001230739190000104
the convex combination coefficient of a gamma operation pole corresponding to the operation point of the ith cogeneration unit in the t scheduling period;
the climbing constraint conditions of the cogeneration unit are as follows:
Figure BDA0001230739190000105
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000106
and
Figure BDA0001230739190000107
respectively the upward climbing capacity and the downward climbing capacity of the ith cogeneration unit, wherein delta T is a scheduling time interval;
the conventional unit operation constraint conditions are as follows:
Figure BDA0001230739190000108
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000109
and
Figure BDA00012307391900001010
respectively the upper limit of active power and the lower limit of active power of the ith conventional unit (obtained from the unit specification);
the conventional unit climbing constraint conditions are as follows:
Figure BDA00012307391900001011
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900001012
and
Figure BDA00012307391900001013
climbing upward for ith conventional unitsAbility and downward climbing ability;
the rotation standby constraint conditions of the conventional unit are as follows:
Figure BDA00012307391900001014
Figure BDA00012307391900001015
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900001016
and
Figure BDA00012307391900001017
respectively performing upward rotation standby and downward rotation standby for the ith conventional unit in the t scheduling period, wherein the upward rotation standby and the downward rotation standby respectively refer to the upward and downward power generation power regulation ranges which can be provided by the generator unit;
the operation constraint conditions of the wind turbine generator are as follows:
Figure BDA00012307391900001018
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900001019
for the available active power of the ith wind power generation unit in the t scheduling period,
Figure BDA00012307391900001020
the actual active power of the wind turbine generator in the t-th scheduling period is obtained;
the power balance constraint of the power system in the combined heat and power system is
Figure BDA0001230739190000111
In the above formula, ILDNumbering sets for loads of the power system, Dm,tIs as followsThe active load of m loads in the t scheduling period;
the constraint conditions of the line transmission capacity of the power system in the combined heat and power system are as follows:
Figure BDA0001230739190000112
in the above formula, IEPSRepresenting a set of node numbers in the power system,
Figure BDA0001230739190000113
and
Figure BDA0001230739190000114
index number sets respectively representing cogeneration units, conventional units, wind turbines and loads connected to the ith node of the power system, ILNRepresenting a set of power system line numbers, LjRepresenting the active transmission capacity of the jth line of the power system;
the rotating standby constraint conditions of the power system are as follows:
Figure BDA0001230739190000115
in the above formula, SRUtAnd SRDtRespectively representing an upward rotation standby and a downward rotation standby of the power system in the t scheduling period;
(1-2-2) operating constraints of a heating system in a combined heat and power system, including:
(1-2-2-1) heating constraint conditions of heat sources including a cogeneration unit and a heat boiler, including:
the constraint conditions of the temperature difference between the heat supply and the water supply and return of the nodes are as follows:
Figure BDA0001230739190000116
in the above formula, set
Figure BDA0001230739190000117
Representing a combined heat and power generation unit and a heat boiler number set connected with a heating system node k, C is the specific heat capacity of water,
Figure BDA0001230739190000118
is the water flow at node k,
Figure BDA0001230739190000119
and
Figure BDA00012307391900001110
respectively representing the water supply temperature and the water return temperature of the node k (each node of the heating system is provided with a water supply pipe and a water return pipe), and integrating
Figure BDA00012307391900001111
Representing a set of nodes connecting heat sources in a heating system;
the constraint condition of the heat supply of the heat boiler, namely the heat supply of the heat boiler needs to be in the upper limit of the heat supply:
Figure BDA00012307391900001112
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900001113
representing the upper limit of the heat production amount of the ith heat boiler;
the constraint condition of the water supply temperature of the heat source node, namely the water supply temperature of the heat source node, needs to be ensured within a certain range:
Figure BDA00012307391900001114
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900001115
and
Figure BDA00012307391900001116
providing an upper limit and a lower limit of water supply temperature for a node k in a heating system;
(1-2-2-2) heat exchange station operating constraints comprising:
the relationship between the heat exchange quantity and the temperature difference between the water supply and the water return of the node is as follows:
Figure BDA0001230739190000121
in the above formula, set
Figure BDA0001230739190000122
Representing the set of heat exchange stations in the heating system connected to node k,
Figure BDA0001230739190000123
representing heat exchange quantity of the nth heat exchange station in the t scheduling period
Figure BDA0001230739190000124
Representing a node set connected with the heat exchange station in the heating system;
the return water temperature of the heat exchange station node needs to be ensured within a safety range:
Figure BDA0001230739190000125
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000126
and
Figure BDA0001230739190000127
respectively setting the upper limit and the lower limit of the return water temperature of a node k in the heating system;
(1-2-2-3) heating network operation constraints comprising:
Figure BDA0001230739190000128
Figure BDA0001230739190000129
in the above formula, the first and second carbon atoms are,
Figure BDA00012307391900001210
and
Figure BDA00012307391900001211
respectively shows the water supply flow and the water return flow from the node k2 to the node k1 in the heating system,
Figure BDA00012307391900001212
and
Figure BDA00012307391900001213
respectively representing the supply pipe node and the return pipe node of node k1 in the heating system,
Figure BDA00012307391900001214
for the ambient temperature of the t-th scheduling period,
Figure BDA00012307391900001215
and
Figure BDA00012307391900001216
respectively shows the heat transfer coefficient of the water supply and the heat transfer coefficient of the water return flowing from the node k2 to the node k1 in the heating system,
Figure BDA00012307391900001217
and
Figure BDA00012307391900001218
the value of (A) is calculated by the following formula:
Figure BDA00012307391900001219
Figure BDA00012307391900001220
wherein the content of the first and second substances,
Figure BDA00012307391900001221
and
Figure BDA00012307391900001222
respectively, representing the unit heat transfer coefficients of the water supply pipe and the water return pipe flowing from the node k2 to the node k1 in the heating system, which can be obtained from the name plate of the water pipe,
Figure BDA00012307391900001223
and
Figure BDA00012307391900001224
respectively representing the lengths of the water supply pipe and the water return pipe flowing from the node k2 to the node k1 in the heating system.
Figure BDA00012307391900001225
And
Figure BDA00012307391900001226
the intermediate temperature variables of the heating system respectively represent the water supply temperature and the water return temperature flowing from the node k2 to the node k1 under the condition of not considering the temperature loss, and the expression is as follows:
Figure BDA00012307391900001227
Figure BDA0001230739190000131
in the above formula, the first and second carbon atoms are,
Figure BDA0001230739190000133
respectively represents the number of the dispatching time periods required by the flow from the node k2 to the node k1 in the water supply pipe and the water return pipe in the heating system, and symbols
Figure BDA0001230739190000134
Represents rounding down;
(2) converting the combined heat and power optimization scheduling model established in the step (1) into a matrix form by using xERepresenting an electric power systemVariables, power system variables including
Figure BDA0001230739190000135
And
Figure BDA0001230739190000136
by xHRepresenting heating system variables including
Figure BDA0001230739190000137
And
Figure BDA0001230739190000138
the combined heat and power optimization scheduling model can be converted into a matrix form as follows:
Figure BDA0001230739190000139
s.t.AExE≤bE
AHxH≤bH
DxE+ExH≤f
in the above formula, CEAnd CHRespectively representing the objective functions of the power system and the heating system, wherein CERepresents
Figure BDA00012307391900001311
CHRepresents
Figure BDA00012307391900001312
Constraint AExE≤bERepresents the constraints of the power system, i.e., all the constraints in step (1-2-1), AE、bEEach row of (A) corresponds to each constraint condition of the power system, each column corresponds to each variable in the power system, wherein AEEach element of (b) is a coefficient of a variable corresponding to the column of the element in the constraint corresponding to the row of the element, bEThe element of each row of (1) is an inequality constant term in the constraint condition corresponding to the element(ii) a Constraint AHxH≤bHRepresenting the constraint conditions of the heat supply system, namely all the constraint conditions except the relation between the heat supply quantity and the temperature difference between the supply water and the return water of the node in the step (1-2-2), AH、bHEach row of (A) corresponds to each constraint condition of the heating system, each column corresponds to each variable in the heating system, wherein AHEach element of (b) being a coefficient of a variable represented by its column in the constraint corresponding to the row of the element, bHThe element of each row of (1) is an inequality constant term in the constraint condition corresponding to the element, and the constraint DxE+ExHF represents a coupling constraint condition of the power system and the heating system, namely a relationship constraint condition of the heating amount and the temperature difference of the water supply and return of the node in the step (1-2-2), wherein each row of D, E, f corresponds to each coupling constraint condition of the power system and the heating system one by one, each column of D corresponds to each variable in the power system one by one, each column of E corresponds to each variable in the heating system one by one, each element of D, E is a coefficient of a variable represented by the column of the element in the constraint condition corresponding to the row of the element, and each element of f is an inequality constant term in the constraint condition corresponding to the element;
(3) solving the matrix-form combined heat and power optimization scheduling model obtained in the step (2) by adopting a decomposition coordination solving algorithm, wherein the solving process is shown in fig. 2 and comprises the following steps:
(3-1) initialization: initializing the iteration number m to 0, initializing the optimal cutting number p to 0, initializing the feasible cutting number q to 0, solving the following optimal scheduling problem of the power system and obtaining the optimal solution
Figure BDA0001230739190000141
Figure BDA0001230739190000142
s.t.AExE≤bE
(3-2) solution according to electric Power System
Figure BDA0001230739190000143
The following heating system problems are solved:
Figure BDA0001230739190000144
s.t.AHxH≤bH
DxE+ExH≤f
Figure BDA0001230739190000146
(3-2-1) if the heating system problem in the step (3-2) is feasible, increasing the number p of optimal cuts by 1 to generate the optimal cuts as shown in the following formula:
Figure BDA0001230739190000147
wherein A isOC＝λT,
Figure BDA0001230739190000148
λ is in step (3-2)
Figure BDA0001230739190000149
The lagrange multiplier of the terms is,
Figure BDA00012307391900001410
the optimal objective function value of the heating system problem in the step (3-2);
(3-2-2) if the heating system problem in step (3-2) is not feasible, increasing the number q of feasible cuts by 1 to generate feasible cuts as shown in the following formula:
Figure BDA00012307391900001411
wherein the parameters
Figure BDA00012307391900001412
And
Figure BDA00012307391900001413
the generation steps are as follows:
(3-2-2-1) the feasibility problem of the heating problem is expressed in the form:
Figure BDA00012307391900001414
s.t.AHxH≤bH
Figure BDA00012307391900001415
(3-2-2-2) relaxation of the feasibility problem in the above step (3-2-2-1) is carried out by introducing a relaxation term, resulting in the following problems:
Figure BDA0001230739190000151
s.t.AHxH≤bH
Figure BDA0001230739190000152
≤0
(3-2-2-3) will bind to A in the relaxor problem in step (3-2-2-2) aboveHxH≤bHAnd constraint
Figure BDA0001230739190000153
The corresponding Lagrange multipliers are respectively marked as
Figure BDA0001230739190000154
And
Figure BDA0001230739190000155
then
Figure BDA0001230739190000156
And
Figure BDA0001230739190000157
respectively as follows:
Figure BDA0001230739190000158
(3-3) solving the power system optimization scheduling problem:
Figure BDA0001230739190000159
s.t.AExE≤bE
CH←E≥0
Figure BDA00012307391900001510
Figure BDA00012307391900001511
increasing the iteration number m by 1 and recording the optimal solution as
Figure BDA00012307391900001512
(3-4) judging the convergence of the iteration if
Figure BDA00012307391900001513
Wherein, if delta is a convergence threshold value and is generally 0.001, terminating the iteration and executing the step (3-5); if it is
Figure BDA00012307391900001514
Returning to the step (3-2) to continue the calculation;
and (3-5) taking the obtained optimal solution as a scheduling parameter of the thermoelectric combined optimization scheduling.

Claims (1)

1. A decomposition coordination scheduling method of a combined heat and power system is characterized by comprising the following steps:
(1) establishing a scheduling model of the combined heat and power system, wherein the scheduling model consists of a target function and constraint conditions, and specifically comprises the following steps:
(1-1) objective function of scheduling model of combined heat and power system:
the objective function of the scheduling model of the combined heat and power system is the minimization of the operation cost of the power system and the heat supply system, and the expression is as follows:
Figure FDA0002557102860000011
in the above formula, T is a set of scheduling periods, ICHP、ITU、IWDAnd IHBRespectively are the number sets of a cogeneration unit, a conventional unit, a wind turbine unit and a heat boiler in a combined heat and power system,
Figure FDA0002557102860000012
and
Figure FDA0002557102860000013
the production cost functions of a cogeneration unit, a conventional unit, a wind turbine generator and a hot boiler in the combined heat and power system are respectively shown, i is the serial number of the cogeneration unit, the conventional unit, the wind turbine generator and the unit or the boiler in the hot boiler, and t is a scheduling time interval;
the production cost function of the cogeneration unit is as follows:
Figure FDA0002557102860000014
in the above formula, the first and second carbon atoms are,
Figure FDA0002557102860000015
and
Figure FDA0002557102860000016
respectively are production cost coefficients which are intrinsic parameters of the cogeneration unit,
Figure FDA0002557102860000017
and
Figure FDA0002557102860000018
respectively the active power and the heat production quantity of the ith cogeneration unit in the t scheduling period;
the production cost function for a conventional unit is:
Figure FDA0002557102860000019
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000110
and
Figure FDA00025571028600000111
is the power generation cost coefficient of the conventional unit, which is the intrinsic parameter of the conventional unit,
Figure FDA00025571028600000112
the active power of the ith conventional unit in the t scheduling period is obtained;
the production cost function of the wind turbine generator is as follows:
Figure FDA00025571028600000113
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000114
the value of the wind abandon penalty factor is determined according to the consumption demand of the wind power, the dispatching center of the power system regulates according to the feedback of the dispatching result,
Figure FDA0002557102860000021
for the available active power of the ith wind power generation unit in the t scheduling period,
Figure FDA0002557102860000022
the actual active power of the wind turbine generator in the t-th scheduling period is obtained;
the production cost function of the thermal boiler is:
Figure FDA0002557102860000023
in the above formula, the first and second carbon atoms are,
Figure FDA0002557102860000024
is the heat production cost coefficient of the heat boiler, is the inherent parameter of the heat boiler,
Figure FDA0002557102860000025
the heat production quantity of the ith heat boiler in the t scheduling period is obtained;
(1-2) constraints of a scheduling model of the cogeneration system, including:
(1-2-1) operating constraints of the power system in the cogeneration system:
the operation constraint conditions of the cogeneration unit are as follows:
Figure FDA0002557102860000026
Figure FDA0002557102860000027
in the above formula, NEiThe number set of the operation poles of the ith cogeneration unit is provided, wherein the operation poles refer to the points formed by the thermal output and the electric output limit of the cogeneration unit,
Figure FDA00025571028600000218
Figure FDA0002557102860000028
respectively the active power and the heat production quantity of the gamma operation pole of the ith cogeneration unit,
Figure FDA0002557102860000029
the convex combination coefficient of a gamma operation pole corresponding to the operation point of the ith cogeneration unit in the t scheduling period;
the climbing constraint conditions of the cogeneration unit are as follows:
Figure FDA00025571028600000210
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000211
and
Figure FDA00025571028600000212
respectively the upward climbing capacity and the downward climbing capacity of the ith cogeneration unit, wherein delta T is a scheduling time interval;
the conventional unit operation constraint conditions are as follows:
Figure FDA00025571028600000213
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000214
and i TUPrespectively setting the upper limit of active power and the lower limit of active power of the ith conventional unit;
the conventional unit climbing constraint conditions are as follows:
Figure FDA00025571028600000215
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000216
and
Figure FDA00025571028600000217
are respectively asThe upward climbing capacity and the downward climbing capacity of the ith conventional unit;
the rotation standby constraint conditions of the conventional unit are as follows:
Figure FDA0002557102860000031
Figure FDA0002557102860000032
in the above formula, the first and second carbon atoms are,
Figure FDA0002557102860000033
and
Figure FDA0002557102860000034
respectively carrying out upward rotation standby and downward rotation standby on the ith conventional unit in the t scheduling period;
the operation constraint conditions of the wind turbine generator are as follows:
Figure FDA0002557102860000035
in the above formula, the first and second carbon atoms are,
Figure FDA0002557102860000036
for the available active power of the ith wind power generation unit in the t scheduling period,
Figure FDA0002557102860000037
the actual active power of the wind turbine generator in the t-th scheduling period is obtained;
the power balance constraint of the power system in the combined heat and power system is
Figure FDA0002557102860000038
In the above formula, ILDNumbering sets for loads of the power system, Dm,tFor the m load at tThe active load size of each scheduling period;
the constraint conditions of the line transmission capacity of the power system in the combined heat and power system are as follows:
Figure FDA0002557102860000039
in the above formula, IEPSRepresenting a set of node numbers in the power system,
Figure FDA00025571028600000310
and
Figure FDA00025571028600000311
index number sets respectively representing cogeneration units, conventional units, wind turbines and loads connected to the ith node of the power system, ILNRepresenting a set of power system line numbers, LjRepresenting the active transmission capacity of the jth line of the power system;
the rotating standby constraint conditions of the power system are as follows:
Figure FDA00025571028600000312
in the above formula, SRUtAnd SRDtRespectively representing an upward rotation standby and a downward rotation standby of the power system in the t scheduling period;
(1-2-2) operating constraints of a heating system in a combined heat and power system, including:
(1-2-2-1) heating constraint conditions of heat sources including a cogeneration unit and a heat boiler, including:
the constraint conditions of the temperature difference between the heat supply and the water supply and return of the nodes are as follows:
Figure FDA0002557102860000041
in the above formula, set
Figure FDA0002557102860000042
Representing a combined heat and power generation unit and a heat boiler number set connected with a heating system node k, C is the specific heat capacity of water,
Figure FDA0002557102860000043
is the water flow at node k,
Figure FDA0002557102860000044
and
Figure FDA0002557102860000045
respectively representing the supply and return water temperatures of node k, set
Figure FDA0002557102860000046
Representing a set of nodes connecting heat sources in a heating system;
constraint conditions of heat supply amount of the heat boiler:
Figure FDA0002557102860000047
in the above formula, the first and second carbon atoms are,
Figure FDA0002557102860000048
representing the upper limit of the heat production amount of the ith heat boiler;
constraint conditions of supply water temperature of heat source nodes:
Figure FDA0002557102860000049
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000410
and
Figure FDA00025571028600000411
providing an upper limit and a lower limit of water supply temperature for a node k in a heating system;
(1-2-2-2) heat exchange station operating constraints comprising:
the relationship between the heat exchange quantity and the temperature difference between the water supply and the water return of the node is as follows:
Figure FDA00025571028600000412
in the above formula, set
Figure FDA00025571028600000413
Representing the set of heat exchange stations in the heating system connected to node k,
Figure FDA00025571028600000414
representing heat exchange quantity of the nth heat exchange station in the t scheduling period
Figure FDA00025571028600000415
Representing a node set connected with the heat exchange station in the heating system;
the return water temperature of the heat exchange station node needs to be ensured within a safety range:
Figure FDA00025571028600000416
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000417
and
Figure FDA00025571028600000418
respectively setting the upper limit and the lower limit of the return water temperature of a node k in the heating system;
(1-2-2-3) heating network operation constraints comprising:
Figure FDA00025571028600000419
Figure FDA0002557102860000051
in the above formula, the first and second carbon atoms are,
Figure FDA0002557102860000052
and
Figure FDA0002557102860000053
respectively shows the water supply flow and the water return flow from the node k2 to the node k1 in the heating system,
Figure FDA0002557102860000054
and
Figure FDA0002557102860000055
respectively representing the supply pipe node and the return pipe node of node k1 in the heating system,
Figure FDA0002557102860000056
for the ambient temperature of the t-th scheduling period,
Figure FDA0002557102860000057
and
Figure FDA0002557102860000058
respectively shows the heat transfer coefficient of the water supply and the heat transfer coefficient of the water return flowing from the node k2 to the node k1 in the heating system,
Figure FDA0002557102860000059
and
Figure FDA00025571028600000510
the value of (A) is calculated by the following formula:
Figure FDA00025571028600000511
Figure FDA00025571028600000512
wherein the content of the first and second substances,
Figure FDA00025571028600000513
and
Figure FDA00025571028600000514
respectively, representing the unit heat transfer coefficients of the water supply pipe and the water return pipe flowing from the node k2 to the node k1 in the heating system, which can be obtained from the name plate of the water pipe,
Figure FDA00025571028600000515
and
Figure FDA00025571028600000516
respectively representing the lengths of a water supply pipe and a water return pipe flowing from a node k2 to a node k1 in the heating system;
Figure FDA00025571028600000517
and
Figure FDA00025571028600000518
the intermediate temperature variables of the heating system respectively represent the water supply temperature and the water return temperature flowing from the node k2 to the node k1 under the condition of not considering the temperature loss, and the expression is as follows:
Figure FDA00025571028600000519
Figure FDA00025571028600000520
Figure FDA00025571028600000521
in the above formula, the first and second carbon atoms are,
Figure FDA00025571028600000523
respectively represents the number of the dispatching time periods required by the flow from the node k2 to the node k1 in the water supply pipe and the water return pipe in the heating system, and symbols
Figure FDA00025571028600000524
Represents rounding down;
(2) converting the combined heat and power optimization scheduling model established in the step (1) into a matrix form by using xERepresenting power system variables including
Figure FDA00025571028600000525
And
Figure FDA00025571028600000526
by xHRepresenting heating system variables including
Figure FDA00025571028600000527
And
Figure FDA00025571028600000528
the combined heat and power optimization scheduling model can be converted into a matrix form as follows:
Figure FDA0002557102860000061
s.t.AExE≤bE
AHxH≤bH
DxE+ExH≤f
in the above formula, CEAnd CHRespectively representing the objective functions of the power system and the heating system, wherein CERepresents
Figure FDA0002557102860000062
CHRepresents
Figure FDA0002557102860000063
Constraint AExE≤bERepresents the constraints of the power system, i.e., all the constraints in step (1-2-1), AE、bEEach row of (A) corresponds to each constraint condition of the power system, each column corresponds to each variable in the power system, wherein AEEach element of (b) is a coefficient of a variable corresponding to the column of the element in the constraint corresponding to the row of the element, bEThe element of each row of (1) is an inequality constant term in the constraint condition corresponding to the element; constraint AHxH≤bHRepresenting the constraint conditions of the heat supply system, namely all the constraint conditions except the relation between the heat supply quantity and the temperature difference between the supply water and the return water of the node in the step (1-2-2), AH、bHEach row of (A) corresponds to each constraint condition of the heating system, each column corresponds to each variable in the heating system, wherein AHEach element of (b) being a coefficient of a variable represented by its column in the constraint corresponding to the row of the element, bHThe element of each row of (1) is an inequality constant term in the constraint condition corresponding to the element, and the constraint DxE+ExHF represents a coupling constraint condition of the power system and the heating system, namely a relationship constraint condition of the heating amount and the temperature difference of the water supply and return of the node in the step (1-2-2), wherein each row of D, E, f corresponds to each coupling constraint condition of the power system and the heating system one by one, each column of D corresponds to each variable in the power system one by one, each column of E corresponds to each variable in the heating system one by one, each element of D, E is a coefficient of a variable represented by the column of the element in the constraint condition corresponding to the row of the element, and each element of f is an inequality constant term in the constraint condition corresponding to the element;
(3) solving the matrix-form combined heat and power optimization scheduling model obtained in the step (2) by adopting a decomposition coordination solving algorithm, wherein the steps are as follows:
(3-1) initialization: initializing the iteration number m to 0, initializing the optimal cutting number p to 0, initializing the feasible cutting number q to 0, solving the following optimal scheduling problem of the power system and obtaining the optimal solution
Figure FDA0002557102860000064
Figure FDA0002557102860000071
s.t.AExE≤bE
(3-2) solution according to electric Power System
Figure FDA0002557102860000072
The following heating system problems are solved:
Figure FDA0002557102860000073
s.t.AHxH≤bH
DxE+ExH≤f
Figure FDA0002557102860000074
(3-2-1) if the heating system problem in the step (3-2) is feasible, increasing the number p of optimal cuts by 1 to generate the optimal cuts as shown in the following formula:
Figure FDA0002557102860000075
wherein the content of the first and second substances,
Figure FDA0002557102860000076
λ is in step (3-2)
Figure FDA0002557102860000077
The lagrange multiplier of the terms is,
Figure FDA0002557102860000078
the optimal objective function value of the heating system problem in the step (3-2);
(3-2-2) if the heating system problem in step (3-2) is not feasible, increasing the number q of feasible cuts by 1 to generate feasible cuts as shown in the following formula:
Figure FDA0002557102860000079
wherein the parameters
Figure FDA00025571028600000710
And
Figure FDA00025571028600000711
the generation steps are as follows:
(3-2-2-1) the feasibility problem of the heating problem is expressed in the form:
Figure FDA00025571028600000712
s.t.AHxH≤bH
Figure FDA00025571028600000713
(3-2-2-2) relaxation of the feasibility problem in the above step (3-2-2-1) is carried out by introducing a relaxation term, resulting in the following problems:
Figure FDA00025571028600000714
s.t.AHxH≤bH
Figure FDA00025571028600000715
≤0
(3-2-2-3) will bind to A in the relaxor problem in step (3-2-2-2) aboveHxH≤bHAnd constraint
Figure FDA0002557102860000081
The corresponding Lagrange multipliers are respectively marked as
Figure FDA0002557102860000082
And
Figure FDA0002557102860000083
then
Figure FDA0002557102860000084
And
Figure FDA0002557102860000085
respectively as follows:
Figure FDA0002557102860000086
(3-3) solving the power system optimization scheduling problem:
Figure FDA0002557102860000087
s.t.AExE≤bE
CH←E≥0
Figure FDA0002557102860000088
Figure FDA0002557102860000089
increasing the iteration number m by 1 and recording the optimal solution as
Figure FDA00025571028600000810
(3-4) judging the convergence of the iteration if
Figure FDA00025571028600000811
Wherein, if delta is a convergence threshold value and the value is 0.001, terminating iteration and executing the step (3-5); if it is
Figure FDA00025571028600000812
Returning to the step (3-2) to continue the calculation;
and (3-5) taking the obtained optimal solution as a scheduling parameter of the thermoelectric combined optimization scheduling.
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